performance | repo contains benchmarks used for testing the performance

I am just curious does batch listener mode in Spring Kafka gives better performance than non-batch listener mode? If we are handling exceptions then we still need to process each record in Batch-listener mode. Non-batch seems less error prone, stable and customizable .

Please share your views on this as I didn't find any good comparison.

I have a store setup that has multiple arrays

I'm trying to search all arrays at once, via a textfield.

I can get this done, by calling a selector function on keyup, that filters the 4 arrays and pushes to a new array.

I've thought about merging all the arrays to one array before filtering, but I want to keep the results separate, as they are going to be displayed in categories.

Just trying to see if I can streamline the performance at all and if there's a more concise way of doing this, in case I need to do something similar with larger arrays.

my textField function:

Trying to use Impact font in my html email, which is working fine in Outlook 365 windows and web clients, as well as Gmail client in browser, but the iOS native Mail app, Gmail app and Outlook apps all default back to arial. What am I missing?

Here's the table in question. Class is leftover from a MS port, and I'm leaving it in in the hopes that it improves mso performance, but all it's really doing is setting default font-family, font-size and margin (0).

I've upgraded to the M1 chip 2020 Macbook Air from a 7th gen. Intel chip pc. Overall, I'm very happy and content with it but when it comes to Android Studio performance, which I use quite often, it is very disappointing I'm sorry to say. When will an Apple Silicon compatible version be available? Are any of you guys have any clue?

I'm beginner in iOS. I want to set UITextview Attribute text whenever user typing, That's ok but the problem is the performance is lag, and too slow. I don't know what happened. I would appreciate any of your help! thanks

I am programming in Python 3.8 with Tensorflow installed along with my natural language processing project. When I want to begin the training phase, I get this message right before I begin...

I have two MLModels in my app. The first one is generating an MLMultiArray output which is meant to be used as the second model input.
As I'm trying to make things as performance-best as possible. I was thinking about using VNImageRequestHandler to feed it with the first model output (MLMultiArray) and use Vision resize and rectOfIntersent to avoid converting the first input to an image, crop features, to avoid the need to convert the first output to image, do everything manually and use the regular image initializer.

Something like that:

I am trying to contribute to a Github Page/Jekyll site and want to be able to visualise changes locally but when I run bundle exec jekyll serve but I get this output:

We are using stream ingestion from Event Hubs to Azure Data Explorer. The Documentation states the following:

The streaming ingestion operation completes in under 10 seconds, and your data is immediately available for query after completion.

I am also aware of the limitations such as

Streaming ingestion performance and capacity scales with increased VM and cluster sizes. The number of concurrent ingestion requests is limited to six per core. For example, for 16 core SKUs, such as D14 and L16, the maximal supported load is 96 concurrent ingestion requests. For two core SKUs, such as D11, the maximal supported load is 12 concurrent ingestion requests.

But we are currently experiencing ingestion latency of 5 minutes (as shown on the Azure Metrics) and see that data is actually available for quering 10 minutes after ingestion.

Our Dev Environment is the cheapest SKU Dev(No SLA)_Standard_D11_v2 but given that we only ingest ~5000 Events per day (per metric "Events Received") in this environment this latency is very high and not usable in the streaming scenario where we need to have the data available < 1 minute for queries.

Is this the latency we have to expect from the Dev Environment or are the any tweaks we can apply in order to achieve lower latency also in those environments? How will latency behave with a production environment loke Standard_D12_v2? Do we have to expect those high numbers there as well or is there a fundamental difference in behavior between Dev/test and Production Environments in this concern?